南京理工大学学报（自然科学版）2018，Vol.42Issue(3)：329-334,351,7.DOI:10.14177/j.cnki.32-1397n.2018.42.03.011
基于遗传算法和支持向量回归的锂电池健康状态预测
Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression
摘要
Abstract
A joint algorithm based on genetic algorithm ( GA ) and support vector regression (GA-SVR)is proposed to improve the prediction accuracy of state of health(SOH)for lithium-ion batteries. GA is used to optimize the hyper-parameters in SVR model. Several chromosomes are initialized randomly by GA-SVR, each includes the hyper-parameters of SVR. The fitness of each chromosome is calculated by a fitness function. The hyper-parameters information of chromosomes is updated by selection,crossover and mutation according to the fitness. A chromosome with the highest fitness is chosen after multiple iterations. The SVR is trained as a prediction model based on the hyper-parameters of the selected chromosome. The experimental results on batteries datasets of National Aeronautics and Space Administration( NASA) of the USA show that the proposed GA-SVR outperforms the four popular SOH predictors, including spectral mixture kernel-Gaussian process regression ( SMK-GPR ) , periodic covariance function-multiscale Gaussian process regression ( P-MGPR ) , squared exponential function-multiscale Gaussian process regression ( SE-MGPR ) , improved particle swarm optimization-support vector regression( IPSO-SVR) .关键词
遗传算法/支持向量回归/锂电池/健康状态/超参数优化Key words
genetic algorithm/support vector regression/lithium-ion batteries/state of health/hyper-parameter optimization分类
信息技术与安全科学引用本文复制引用
刘皓,胡明昕,朱一亨,於东军..基于遗传算法和支持向量回归的锂电池健康状态预测[J].南京理工大学学报（自然科学版）,2018,42(3):329-334,351,7.基金项目
国家自然科学基金(61772273 （）
61373062) （）
